A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Table 6
The results of compare forecasting models under percentage spilt (dataset partition into 66% training data and 34% testing data) after variable selection.
Methods
Index
RBF Network
Kstar
Random Forest
IBK
Random Tree
After variable selection
Delete the rows with missing data
CC
0.033
0.638
0.729
0.251
0.545
MAE
0.182
0.121
0.111
0.199
0.135
RMSE
0.229
0.176
0.156
0.287
0.212
RAE
0.992
0.657
0.602
1.083
0.736
RRSE
1.007
0.775
0.688
1.262
0.935
Series mean
CC
0.107
0.661
0.739
0.242
0.551
MAE
0.172
0.107
0.101
0.179
0.129
RMSE
0.221
0.167
0.151
0.268
0.205
RAE
0.988
0.615
0.579
1.027
0.740
RRSE
0.995
0.753
0.678
1.208
0.923
Linear
CC
0.105
0.666
0.735
0.258
0.596
MAE
0.173
0.106
0.100
0.175
0.120
RMSE
0.221
0.166
0.151
0.266
0.196
RAE
0.987
0.606
0.572
1.002
0.683
RRSE
0.995
0.748
0.681
1.198
0.883
Median of nearby points
CC
0.106
0.666
0.740
0.264
0.553
MAE
0.173
0.107
0.100
0.177
0.127
RMSE
0.221
0.166
0.151
0.266
0.207
RAE
0.987
0.611
0.571
1.013
0.723
RRSE
0.995
0.747
0.677
1.195
0.932
Mean of nearby points
CC
0.1059
0.667
0.745
0.249
0.540
MAE
0.173
0.107
0.099
0.179
0.129
RMSE
0.221
0.166
0.149
0.268
0.214
RAE
0.987
0.611
0.565
1.025
0.735
RRSE
0.995
0.747
0.672
1.207
0.962
Regression
CC
0.107
0.663
0.739
0.242
0.559
MAE
0.172
0.106
0.101
0.179
0.126
RMSE
0.221
0.167
0.151
0.268
0.200
RAE
0.987
0.610
0.581
1.027
0.723
RRSE
0.994
0.752
0.678
1.207
0.900
denotes after variable selection with enhancing performance; denotes the best performance among 5 models after variable selection.
